Data quality is one of the essential parts of data processing processing flows to enable accuracy and reliability reliability for actionable usage data/events. For example, produce clean and valid billable usage records to Cloud Billing Systems.However, sometime discarding the erroneous data while cleaning the data is not the ideal option. These data could be a potential revenue that are billable but misconfigured from the data source or in the processing logic.
Data Correction is the feature provides by the Usage Engine to automate all data quality and ensure the usage events do not fall out of the processing wheel. It is designed and built to prevent revenue leakage.It could be enabled in the Validate Function when erroneous records are accounted and repaired automatically or through resubmission.
When enabled in Validate Function, the detected erroneous records are routed to Data Correction repository. From the Data Correction Dashboard, users could search, view, modify and resubmit the erroneous data for reprocessing the data in the same stream.
Data Correction. Maintaining high-quality data is crucial for producing accurate results, preventing revenue losses, and building trust in the data. Data quality issues may occur for many reasons like data source not complete or misconfigured processing logic. It is important to correct the data before it is used to create a single source of truth for billing or any other similar purpose.
Data correction is the feature provided by the Usage Engine Cloud Edition to identify and gain access to corrupt or invalid data.
Data can be validated using a validate function which can be easily configured to check one or multiple fields on the data. The records that fail validation rules while execution are accumulated in the data correction. Using Data Correction UI, users can view, correct, re-process, and delete individual or multiple invalid records.